- Company Name
- HFG Insurance Recruitment
- Job Title
- Actuarial Data Scientist
- Job Description
-
**Job Title:** Actuarial Data Scientist
**Role Summary:** Develop and implement predictive analytics, statistical models, and data pipelines to enhance underwriting decisions and support transformation initiatives within a commercial insurer’s central analytics function.
**Expectations:** Deliver actionable insights, high‑quality models, and data‑driven recommendations; collaborate cross‑functionally; adhere to actuarial best practices and data governance standards.
**Key Responsibilities:**
- Design, build, and validate actuarial models (pricing, loss reserving, risk classification) using machine learning and statistical techniques.
- Extract, transform, and load large datasets; maintain data quality and integrity.
- Translate business requirements into analytical solutions and communicate findings to underwriting stakeholders.
- Participate in transformation projects, driving automation and standardisation of analytics processes.
- Monitor model performance, conduct post‑deployment reviews, and iterate for continuous improvement.
- Contribute to documentation, model governance, and regulatory compliance.
**Required Skills:**
- Proficiency in Python (pandas, scikit‑learn, PySpark) and/or R, with SQL for data querying.
- Strong statistical and actuarial modeling expertise (e.g., GLM, survival analysis, stochastic reserving).
- Experience with machine learning pipelines, version control (Git), and cloud data platforms (AWS, Azure, GCP).
- Knowledge of actuarial principles, insurance underwriting, and risk assessment.
- Excellent analytical thinking, problem‑solving, and stakeholder communication.
**Required Education & Certifications:**
- Bachelor’s or Master’s degree in Actuarial Science, Statistics, Mathematics, or Data Science.
- Valid actuarial exam(s) (e.g., SOA/ CAS) preferred; or equivalent professional data science certification (e.g., Microsoft Certified: Azure Data Scientist Associate, AWS Certified Machine Learning – Specialty).